HOUSING STARTS Privately-owned housing starts in April were at a seasonally adjusted annual rate of 1,032,000. This is 8.2 percent (±14.5%)* above the revised March estimate of 954,000, but is 30.6 percent (±6.7%) below the revised April 2007 rate of 1,487,000.

As is so often the case, the devil is in the details:

As far as the April Hosuing Starts go, a monthly change (seasonally adjusted annual rate) was 8.2%, versus an
estimated relative standard error of ±14.5%. Hence, the monthly change is not statistically significant; that is, it is uncertain whether there was an increase or decrease in Housing Starts from March to April.

As to the 30.6% year over year drop — that is ±6.7% — and therefore is statistically significant.

BR, sorry for the off topic query, I was looking for an old post of yours re subprime trading losses, MS specific. Turned up a bunch via the search function, not the one I was looking for.
Re housing, is FHA the next subprime? Anecdotally, the remaining mortgage brokers in business seem to think so.

Great stuff Barry! Having been a statistician for most of the past 20+ years (now a currency trader and fundamentals analyst/coach), I totally appreciate seeing anyone bringing the concept of error to the masses.

So many otherwise intelligent people focus only on estimates such as averages or differences between averages, which only tell part of the story.

Why the quibbling over statistical nuances, as if one or another conclusion is going to amount to a hill of beans?

Just go pull up a long-term chart on housing starts and you’ll see that, generally, we’ve had housing starts in this zone in 1966, 1974-75, 1980-82, 1991 and now now.

In those past cases, such lackluster readings have been part of a bottom. I suspect this time is NOT different. In my mind the only question is how long this takes to mend itself. My sense is, longer rather than shorter.

My econometrics is a few years ago but the distribution of the sample will generate the standard error depending on widely it’s dispersed around the mean; i.e. the S.E. is calculable from the data in the best of my recollection. All that aside there are really two important things – first off the Starts data is Single + Multi-family starts and the latter is very volatile while the former is still dropping like a rock and is the majority of the total starts. More importantly the S.E. noise issue goes away when you look at the time-series and trend, by eyeball if necessary, but YoY changes are a great proxy. You can look at the data or dload it yourself at the STL Fed FREDII site which has also a very nice little graphing facility. If you’ll trust my spreadsheet skills YOY% change for starts -30.6%, less than Mar. 36.1% but 1-family’s went from 41.1% to 42.2%; i.e. the cliff-diving is accelerating. In any case -20, -30, -40 it’s all painful.

If the estimate is more survey than estimate, the margin could just be reporting a high variance in the data, not so much “error” in the data. Sorry, too much to do today to actually read the report methodology… just throwing that out as a general comment.

In theory, housing starts should be “negative”. What? That’s right, they should be tearing them down. Why aren’t they tearing them down?

“The tooth fairy is out of town. She sold her San Francisco condo and now resides in Baja California. She left no forwarding address.

When the cat’s away, the mice will play. From 1996–2006, the cat was away. The mice played. What did they play? Fast and loose.

I received this note from John Schaub, whose site, http://www.JohnSchaub.com, is one of the best sources of information on single-family home investing.

There was a lot of mortgage fraud in the intercity areas of many big cities like Atlanta. They would take a house worth $5000 and sell it for $50,000 to a friend who would get a loan based on the $50,000. That did not make the house worth $50,000 but the tax assessor would pick up the new “value” off the recorded deed. Other properties were sold with owner financing at terrifically inflated prices, then the loans (first mortgages on single family houses – how could you go wrong) were sold to investors. I’ve long advised never to buy a loan unless you are willing to go see the property. Many of these houses have negative value. It would cost more to tear the house down than the lot is worth.”
Gary North

Emanuelle – have you reviewed CalculatedRisk’s work on Res. Investment and Non-Res, etc. ? You might find it very worthwhile. His latest on Non-Res, it’s relation to RI and the impact on the Economy can be found here:http://tinyurl.com/5xkjvt

As CR points out RI is a major leading indicator for business cycles – IMHO largely because Investment in general is the swing factor, or the accelerator as they used to call it, in the macro-econ stuff. Besides that the Housing ATM was generating ~ $800B/year in consumption expenditures thru MEW.

Barry is therefore, again of course IMHO, definitely right to be worried about Housing.

I cannot match a growth in disposable income with the following reporting:

By SUSAN SALISBURY
Palm Beach Post Staff Writer

Thursday, May 15, 2008

“A recent national survey found about 83 percent of consumers use credit cards to buy gas, up from 55 percent to 60 percent five years ago, said Jim Smith, chief executive officer and president of the Florida Petroleum Marketers and Convenience Store Association in Tallahassee. Credit card fees are soaring along with gas prices.

‘Visa and MasterCard are making more per gallon than the retailer,’ Smith said.”

When consumers are forced to use credit for essential purchases, it is not consistent with increased disposable income.

Winston, could it be that much of the increase just comes from the convenience of using a card?

I spend maybe $50 in cash per month. I put everything thru the card.

What are the relative contributions of these two factors (desperation vs convenience) to the trend you see? (I am not trying to give you or anyone here a “homework assignment”. Personally, I don’t know and don’t have the time to look either.)

Even though “-2.2” is within the “14.5%” deviation from “8.2”, does the difference mean anything at all to you? If so, is the difference between those values “meaningful” to a statistician? Is the difference “significant”?

Yes, I’m asking what may or may not be repetitive questions. Why? Because depending on how some terms are supposed to be defined (on which I’m not an authority), I can imagine different answers.

~~~

BR: Neither is statistically significant — yet another reason to emphasize year over year data

With ATM fees sharply increasing ($2-3) to get cash, I am trying to put everything on the credit card (American Express with cash back is my favorite). I definitely use more of plastic this year, pay the entire balance in full and get nice cash back each month. I have no debt, but I use plastic more often because of convenience, hate ATM fees, get cash back, etc.

Personally, my disposable income and savings rate are growing nicely. I feel no recession, none what so ever. I guess other people are in trouble (at least this is what the media has been singing lately), but I do not see any slowdown (knock on wood).

Wunsacon wrote, “Winston, could it be that much of the increase just comes from the convenience of using a card?”

I would think there is a difference between credit card use and cash card use. Myself, I pay for almost everything with a cash card – but there is a substantial difference between having the money in your checking account and using a convenience card and having no money in your checking account and being forced to used your credit card.

I can’t say what the reason for the sharp increase that was reported by the article, but it suggests to me less ability to pay than more. 5 years ago was not the dark ages, and to believe that a 25% or so increase in usage was due to a “sudden” realization of the convenience of credit card use doesn’t strike me as a likely explanation.

However, I have been wrong before and will be so again. Of that I am sure.

Don’t you believe that it is not interesting. I love factual detail, just don’t have the time to keep up with all the people lying to me. I spend a lot of time sorting out the lies in my own line of work. Love hearing about it from other quarters.

Winson, et.al. the way I read the surge in consumer debt, including cards, is that people were already living beyond their incomes via the housing ATM and now, either keeping up the spending or because of lost jobs, etc. are charging normal consumption purchases. Which makes sense if you think it’s a temporary thing but is personally dangerous and macro-economically scary when the bills start coming due. Not to mention that the consumption components of the GDP haven’t in total tanked yet but only services held up.

Why did the gas stations make it so convenient to use the charge card when:

a. It lets the consumer come and go without ever going into the store to be tempted on the high margin items.

It doesn’t have to play out that way. If the customer needs something from the minimart at the gas station then the switching costs of making yet another stop on the way home are still much higher than simply going inside for another transaction…. and wonder of wonders, because all the gas-only customers are not clogging up line for the till, the line is short enough to reinforce to the mini-grocery customer that the gas station quick buy is worth the extra price you pay.

But, how ’bout this: Many times, people just take the “quick $100” from the ATM. In the past, a fillup used to be maybe $20 or 1/5 of what they took out. Now, it’s nearly half. Better to “put it on the tab” than to have to go right back to the ATM again or carry around so much cash just for gas.

Nevertheless, I don’t believe people have more disposable income either, certainly not in real terms.

It demonstrates clearly how biased you are in estimation of data. I have looked through several on your previous posts and found that you did not mention any S.E. in them despite changes in relevant variables were also inside 1-sigma. Effectively, the only thing what you could claim in these previous posts is that data are not reliable to draw any conclusion.

I would propose to treat this current post as a big trap which you have digged with own hands. This post should provide a Golden Standard for any of your future (and past , we can judge now how reliable were your statements – are they “statistically significant”) posts. As a consistent researcher and analyst you must follow up this high standard of statistical significance and robustness.

I think you need an experienced opponent to check you posts before publishing for side effects.

This leaves me scratching my head as to whether you are serious about statistical error, or merely an asshat.

Everyone is entitled to their own opinions, but not their own facts. And in case you missed our terms of use, anyone that pollutes the comment stream with false and easily verifiable information, will lose their welcome here.

For the sake of accuracy I have to admit that I was wrong in the definition of the bias.
Before I leave this blog, I just ask for the fairness. If you could please to allow this last post. (Actually, it is addressed to you, mainly)

1. from 7 links you gave in your previous coment only 2 are associated with S.E. for decreasing housing sales. In one case, S.E. is below the observed change. So, you have mentioned S.E. only one time (September 27, 2007) commenting not significant negative change in these 7 posts.

2. 5 links lead to posts which use S.E. to deny any significance of positive changes.

3. I rechecked some previous months in 2008 and late 2007. No S.E. is reported for statistically not sinificant negative changes (except the one given in your link.

4. I do not call this observation “bias” anymore. My statistics on your posts is just my opinion.

It’s worth considering what “statistically significant” means. A phrase like “8.2 percent (±14.5%)” implies a percentage confidence. This is often 95%, but is not necessarily. Assuming 95% confidence, this means that, based on the data currently avaiable, it is 95% likely that the change in housing starts from March to April was between -6.3% and +22.7%.

This implies several things. 5% of the time, the actual change will be either less that -6.3% or greater that +22.7%. If you are making an investment based on the change being in that range, there is still a 5% risk that you are starting from a faulty assumption.

One way of restating this is that if the number were to be 8.2 percent (±8.2%), this result may be “statistically significant”, but there’s still a 2.5 percent chance of negative growth (assuming symmetric distribution, the result will be on the low side of the distribution range half of 5% of the time).

If you know what the probability distribution looks like, you can determine the probability that the outcome is in any particular range. If you are more interesting in the question of whether the change is positive or negative than in what the actual value is, you can estimate a probability based on the stated value and the size of the error.

In the case of 8.2 percent (±14.5%), it is not certain that the actual value is positive, but the probability that the value is positive is well over 50%. (I can’t do the calculation off the top of my head, and any calculation would require making assumptions about the shape of the distribution curve, but I’m guessing this range gives around an 80% chance of an overall positive monthly change.